a frequency table is just a table of results showing how frequently something occurred across one or multiple categories (rate of occurrence).

Most commonly, line graphs are used to show changes over time. Line graphs provide a visual representation that allows us to see peaks and troughs in data that is connected in some way.

Bar charts are diagrams that represent frequencies of non-continuous data. Any data that is provided in discrete (separate) categories can be placed into a bar chart, including the type of data we just discussed in our section on line graphs.

Diagrams called histograms show how often occurring values for continuous data are distributed. They display data that has been measured on the y-axis by frequency density, unlike bar charts, which do not. Similar to a bar chart, the data is represented by bars that emerge between measurements, essentially displaying all outcomes that fall within that range (officially referred to as a class interval) on the x-axis. Except when a certain class interval has no results, each bar hits the one after it and there are no gaps. Histograms are particularly effective in displaying data on weight, height, and duration.

Pie charts are diagrams which represent data proportionately, as part of a whole picture of responses. We would usually only use a pie chart to indicate percentages of the population/results across a number of categories. Pie charts help us to visually compare what proportion of the overall results fall into each discrete category.

A link or correlation between two or more co-variables is represented via scatter diagrams, often known as graphs. Scatter diagrams use an x to represent single spots on the graph that represent distinct findings. As a preliminary to (the step before) inferential statistical testing, you can examine the strength and direction of any potential association between the co-variables once all results have been plotted.

Distribution curves are linear representations of data that include a mean, median and modal score to show a spread of data. Distribution curves visually display statistics as a graph/chart. There are 3 main types: normal distribution, positive skewed distribution and negative skewed distribution

Sometimes data can be skewed in its distribution (i.e. not central). When this is the case, the mode and mean do not fall in the centre of the distribution curve, as they do in a normal distribution/bell curve. In a positive (right) skewed distribution, the most common score (mode) is lower than the median, which is also lower than the mean.
In a negative (left) skewed distribution the most common score (mode) is higher than the median, which is also higher than the mean.